{"id":18654,"date":"2026-02-27T07:44:55","date_gmt":"2026-02-27T07:44:55","guid":{"rendered":"https:\/\/www.fibermall.com\/blog\/?p=18654"},"modified":"2026-02-27T07:44:58","modified_gmt":"2026-02-27T07:44:58","slug":"analysis-of-core-port-ratio-and-convergence-in-intelligent-computing-center-and-gpu-cluster-network-design","status":"publish","type":"post","link":"https:\/\/www.fibermall.com\/blog\/gpu-cluster-network-design.htm","title":{"rendered":"Analysis of Core Port Ratio and Convergence in Intelligent Computing Center and GPU Cluster Network Design"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.fibermall.com\/blog\/gpu-cluster-network-design.htm\/#Core_Port_Ratio\" >Core Port Ratio<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.fibermall.com\/blog\/gpu-cluster-network-design.htm\/#Network_Convergence\" >Network Convergence<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.fibermall.com\/blog\/gpu-cluster-network-design.htm\/#Interrelationships_and_Design_Trade-offs\" >Interrelationships and Design Trade-offs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.fibermall.com\/blog\/gpu-cluster-network-design.htm\/#Summary_Comparison_Table\" >Summary Comparison Table<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Core_Port_Ratio\"><\/span><strong>Core Port Ratio <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>Definition:<\/strong>&nbsp;Core port ratio refers to the proportional relationship between the ports on network devices (primarily switches) used to connect&nbsp;Computing Nodes (GPU Servers) and the ports used for Internal Network Interconnection when building GPU clusters (e.g., for AI training, HPC).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Access\/Leaf Ports:<\/strong>\u00a0Ports used to directly connect GPU servers. Each GPU server typically connects to the network via multiple high-speed network cards (e.g., an 8-card server equipped with eight <a href=\"https:\/\/www.fibermall.com\/store-21983-200gb-nic.htm\" target=\"_blank\" rel=\"noreferrer noopener\">200G NICs<\/a>).<\/li>\n\n\n\n<li><strong>Uplink\/Spine\/Trunk Ports:<\/strong>\u00a0Ports used to connect to upper-layer switches or for switch-to-switch interconnection to form a larger-scale non-blocking network. In a Spine-Leaf architecture, these are the ports on Leaf switches connecting to Spine switches.<\/li>\n<\/ul>\n\n\n\n<p><strong>The Core Ratio Problem:<\/strong>&nbsp;The question is: Given a switch&#8217;s (usually a Leaf switch) total number of ports, how many should be allocated to servers and how many should be reserved for uplinks?<\/p>\n\n\n\n<p><strong>Common Ratio Examples:<\/strong><strong><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>1:1:<\/strong>\u00a0For example, on a 128-port Leaf switch, 64 ports connect to servers and 64 ports connect up to Spines. This is the classic ratio for achieving\u00a0a Full Bandwidth, Non-blocking network, ensuring sufficient vertical bandwidth for communication between any two servers.<\/li>\n\n\n\n<li><strong>2:1 or 3:1:<\/strong>\u00a0For example, on a 128-port switch, 96 ports connect to servers and 32 connect up. This implies a certain degree of\u00a0Oversubscription.<\/li>\n\n\n\n<li><strong>Variable Ratios:<\/strong>\u00a0In modern hyperscale clusters, ratios may be achieved through multiple layers and combinations of different port speeds to allow for more flexible designs.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Common-Ratio-Examples.jpg\" alt=\"Common Ratio Examples\" class=\"wp-image-18658\" style=\"width:800px\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Common-Ratio-Examples.jpg 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Common-Ratio-Examples-300x164.jpg 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Common-Ratio-Examples-768x419.jpg 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Function:<\/strong><strong><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Determines Single-Point Bandwidth Limit:<\/strong>\u00a0It directly determines the maximum bandwidth with which a single GPU server can simultaneously communicate with all other servers in the cluster.<\/li>\n\n\n\n<li><strong>Balances Cost and Performance:<\/strong>\u00a0A higher uplink port ratio (e.g., 1:1) offers optimal performance but results in the highest network equipment costs and port consumption. A lower ratio (e.g., 3:1) can save costs and support more servers under the same Leaf, but may create bottlenecks on the uplink.<\/li>\n\n\n\n<li><strong>Influences Network Architecture Scale:<\/strong>\u00a0The ratio, combined with the port count of Spine switches, determines the maximum number of servers the entire cluster can support.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Network_Convergence\"><\/span><strong>Network Convergence <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>Contextual Definition:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>In general networking, Convergence usually refers to the time required for a network to recover from topology changes (like link failures or device reboots), recalculate\/sync optimal paths, and reach a stable state. While fast convergence is vital for high availability, in the context of HPC and GPU Clusters, Network Convergence has a more specific, core meaning.<\/li>\n<\/ul>\n\n\n\n<p><strong>Network Convergence Ratio:<\/strong>&nbsp;This refers to the ratio of Total Downstream Bandwidth to Total Upstream or Peer Interconnect Bandwidth at a network aggregation point. It measures potential congestion points in the network.<\/p>\n\n\n\n<p><strong>Formula:<\/strong>&nbsp;Convergence Ratio = Total Access Bandwidth \/ Total Uplink Bandwidth<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Convergence Ratio = 1:1:<\/strong>\u00a0Termed a &#8220;Non-blocking&#8221; or &#8220;No Oversubscription&#8221; network. At any given moment, if all access devices communicate\u00a0at full bandwidth, no bottleneck will occur on the uplink. This is the gold standard for high-performance GPU clusters.<\/li>\n\n\n\n<li><strong>Convergence Ratio > 1:1 (e.g., 3:1):<\/strong>\u00a0Termed an &#8220;Oversubscription&#8221; network. This means if all downstream devices communicate at full load simultaneously, the upstream bandwidth is insufficient to carry the traffic, inevitably causing congestion and queuing. This value (e.g., 3:1) is the convergence ratio.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Network-Convergence-Ratio.jpg\" alt=\"Network Convergence Ratio\" class=\"wp-image-18657\" style=\"width:800px\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Network-Convergence-Ratio.jpg 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Network-Convergence-Ratio-300x164.jpg 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Network-Convergence-Ratio-768x419.jpg 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Function:<\/strong><strong><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Quantifies Network Congestion Risk:<\/strong>\u00a0The convergence ratio is a key metric for measuring whether a network design meets application requirements. A low convergence ratio (close to 1:1) implies lower latency and higher predictability.<\/li>\n\n\n\n<li><strong>Core Lever for Cost Control:<\/strong>\u00a0Allowing a certain degree of oversubscription (e.g., 2:1) is the primary means of reducing network construction costs, as it serves more servers with fewer uplinks and core devices.<\/li>\n\n\n\n<li><strong>Strong Correlation with Application Traffic Patterns:<\/strong>\u00a0If the application traffic pattern is not &#8220;all-to-all&#8221; full bandwidth communication (e.g., primarily server-to-storage traffic, or computing tasks with communication intervals), a higher convergence ratio may be acceptable.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Interrelationships_and_Design_Trade-offs\"><\/span><strong>Interrelationships and Design Trade-offs <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>Means vs. Goal:<\/strong>&nbsp;Port ratio is the means to achieve a specific network convergence ratio, whereas the convergence ratio is one of the core goals the port ratio design aims to achieve.<\/p>\n\n\n\n<p><strong>Direct Determining Relationship:<\/strong>&nbsp;In a simple two-layer Spine-Leaf architecture, the port ratio of the Leaf switch directly determines the network convergence ratio of the first level (Leaf layer).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If a Leaf switch adopts a 1:1 Port Ratio (half ports to servers, half ports up to Spines), then from the perspective of that Leaf switch, its Convergence Ratio is 1:1 (Non-blocking).<\/li>\n\n\n\n<li>If a 3:1 Port Ratio is adopted (assuming identical port speeds), then its Convergence Ratio is 3:1.<\/li>\n<\/ul>\n\n\n\n<p><strong>Multi-layer Accumulation:<\/strong>&nbsp;In large clusters, convergence can occur at multiple levels. For example, multiple GPUs inside a server interconnect via NVLink, then connect to a Leaf via NICs, then Leaf uplinks to Spine, and Spines may have further interconnections. The&nbsp;overall, end-to-end effective convergence ratio is the aggregate result of convergence at all levels. The design goal is usually to ensure the &#8220;Critical Path&#8221; (e.g., GPU-to-GPU communication path) has a convergence ratio of 1:1.<\/p>\n\n\n\n<p><strong>Deep Coupling with GPU Cluster Communication Patterns:<\/strong><strong><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>All-Reduce Dominance:<\/strong>\u00a0In modern AI training (especially Large Models), All-Reduce collective communication is the dominant pattern. This requires efficient paths between any two GPUs. If the network has a high convergence ratio, the uplink will easily become a bottleneck during the &#8220;many-to-many&#8221; communication phase of All-Reduce, severely slowing down training.<\/li>\n\n\n\n<li><strong>Design Implications:<\/strong>\u00a0Therefore, for GPU clusters designed for AI training, the Compute Plane Network\u00a0(used for GPU-to-GPU communication) strongly pursues 1:1 Non-blocking Convergence. This is typically achieved through a 1:1 Port Ratio combined with network topologies like Clos\/Fat-Tree. Storage planes and management networks, however, may use higher convergence ratios to save costs.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" width=\"1024\" height=\"434\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Deep-Coupling-with-GPU-Cluster-Communication-Patterns.jpg\" alt=\"Deep Coupling with GPU Cluster Communication Patterns\" class=\"wp-image-18656\" style=\"width:800px\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Deep-Coupling-with-GPU-Cluster-Communication-Patterns.jpg 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Deep-Coupling-with-GPU-Cluster-Communication-Patterns-300x127.jpg 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2026\/02\/Deep-Coupling-with-GPU-Cluster-Communication-Patterns-768x326.jpg 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Summary_Comparison_Table\"><\/span><strong>Summary Comparison Table <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Feature<\/strong><\/td><td><strong>Network Core Port Ratio<\/strong><\/td><td><strong>Network Convergence (Ratio)<\/strong><\/td><\/tr><tr><td><strong>Essence<\/strong><\/td><td>Physical Design Decision: How switch ports are allocated and used.<\/td><td>Performance Metric\/Concept: The proportional relationship between bandwidth supply and demand.<\/td><\/tr><tr><td><strong>Focus<\/strong><\/td><td>The division and connection methods of device port resources.<\/td><td>The degree of overload or redundancy in network link capacity.<\/td><\/tr><tr><td><strong>Manifestation<\/strong><\/td><td>&#8220;This Leaf switch has 64 server ports and 32 uplink ports.&#8221;<\/td><td>&#8220;From server aggregation to Spine, the network convergence ratio is 2:1.&#8221;<\/td><\/tr><tr><td><strong>Function<\/strong><\/td><td>Implements network topology; balances single-point access density with uplink capacity.<\/td><td>Quantifies potential network performance bottlenecks; key for balancing cost vs. performance.<\/td><\/tr><tr><td><strong>Relationship<\/strong><\/td><td>Port ratio is the physical means to achieve the target convergence ratio. In a single layer, the ratio values are numerically equal.<\/td><td>Convergence ratio is the core standard for evaluating whether the port ratio design meets application requirements.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Conclusion:<\/strong>&nbsp;In GPU cluster network design,&nbsp;one must determine the target convergence ratio (usually pursuing 1:1 non-blocking) based on upper-layer applications (specifically AI training jobs), and then achieve this goal through meticulous port ratio and topology design (such as Fat-Tree, Dragonfly+). Understanding the concepts of &#8220;Port Ratio&#8221; and &#8220;Network Convergence&#8221; and their relationship is the foundation for designing or evaluating a high-performance GPU cluster network. Simply put, port ratio is &#8220;how to do it,&#8221; and convergence ratio is &#8220;why to do it this way and what the effect is.&#8221;<\/p>\n\n\n\n<p><\/p>\n<style>\r\n\r\n        .lwrp.link-whisper-related-posts{\r\n            \r\n            margin-top: 40px;\nmargin-bottom: 30px;\r\n        }\r\n        .lwrp .lwrp-title{\r\n            \r\n            \r\n        }\r\n        .lwrp .lwrp-description{\r\n            \r\n            \r\n\r\n        }\r\n        .lwrp .lwrp-list-container{\r\n        }\r\n        .lwrp .lwrp-list-multi-container{\r\n            display: flex;\r\n        }\r\n        .lwrp .lwrp-list-double{\r\n            width: 48%;\r\n        }\r\n        .lwrp .lwrp-list-triple{\r\n            width: 32%;\r\n        }\r\n        .lwrp .lwrp-list-row-container{\r\n            display: flex;\r\n            justify-content: space-between;\r\n        }\r\n        .lwrp .lwrp-list-row-container .lwrp-list-item{\r\n            width: calc(100% - 20px);\r\n        }\r\n        .lwrp .lwrp-list-item:not(.lwrp-no-posts-message-item){\r\n            \r\n            list-style: decimal;\r\n        }\r\n        .lwrp .lwrp-list-item img{\r\n            max-width: 100%;\r\n            height: auto;\r\n        }\r\n        .lwrp .lwrp-list-item.lwrp-empty-list-item{\r\n            background: initial !important;\r\n        }\r\n        .lwrp .lwrp-list-item .lwrp-list-link .lwrp-list-link-title-text,\r\n        .lwrp .lwrp-list-item .lwrp-list-no-posts-message{\r\n            \r\n                \r\n        }\r\n        @media screen and (max-width: 480px) {\r\n            .lwrp.link-whisper-related-posts{\r\n                \r\n                \r\n            }\r\n            .lwrp .lwrp-title{\r\n                \r\n                \r\n            }\r\n            .lwrp .lwrp-description{\r\n                \r\n                \r\n            }\r\n            .lwrp .lwrp-list-multi-container{\r\n                flex-direction: column;\r\n            }\r\n            .lwrp .lwrp-list-multi-container ul.lwrp-list{\r\n                margin-top: 0px;\r\n                margin-bottom: 0px;\r\n                padding-top: 0px;\r\n                padding-bottom: 0px;\r\n            }\r\n            .lwrp .lwrp-list-double,\r\n            .lwrp .lwrp-list-triple{\r\n                width: 100%;\r\n            }\r\n            .lwrp .lwrp-list-row-container{\r\n                justify-content: initial;\r\n                flex-direction: column;\r\n            }\r\n            .lwrp .lwrp-list-row-container .lwrp-list-item{\r\n                width: 100%;\r\n            }\r\n            .lwrp .lwrp-list-item:not(.lwrp-no-posts-message-item){\r\n                \r\n                \r\n            }\r\n            .lwrp .lwrp-list-item .lwrp-list-link .lwrp-list-link-title-text,\r\n            .lwrp .lwrp-list-item .lwrp-list-no-posts-message{\r\n                \r\n                    \r\n            }\r\n        }<\/style>\r\n<div id=\"link-whisper-related-posts-widget\" class=\"link-whisper-related-posts lwrp\">\r\n            <h3 class=\"lwrp-title\">Related Posts<\/h3>    \r\n        <div class=\"lwrp-list-container\">\r\n                                            <ul class=\"lwrp-list lwrp-list-single\">\r\n                    <li class=\"lwrp-list-item\"><a href=\"https:\/\/www.fibermall.com\/questions\/cx7-xdr-cr8-transceiver.htm\" class=\"lwrp-list-link\"><span class=\"lwrp-list-link-title-text\">Can CX7 NDR Support CR8 Transceiver Modules?<\/span><\/a><\/li><li class=\"lwrp-list-item\"><a href=\"https:\/\/www.fibermall.com\/news\/broadcom-100g-pam4-dsp-for-400g-module.htm\" class=\"lwrp-list-link\"><span class=\"lwrp-list-link-title-text\">Broadcom 100G PAM4 DSP helps the application of 400G module<\/span><\/a><\/li><li class=\"lwrp-list-item\"><a href=\"https:\/\/www.fibermall.com\/blog\/800g-ethernet-technology.htm\" class=\"lwrp-list-link\"><span class=\"lwrp-list-link-title-text\">800G Ethernet Technology<\/span><\/a><\/li><li class=\"lwrp-list-item\"><a href=\"https:\/\/www.fibermall.com\/blog\/infiniband-cable.htm\" class=\"lwrp-list-link\"><span class=\"lwrp-list-link-title-text\">Understanding Infiniband Cable: A Comprehensive Guide<\/span><\/a><\/li><li class=\"lwrp-list-item\"><a href=\"https:\/\/www.fibermall.com\/blog\/interconnection-technology-data-center.htm\" class=\"lwrp-list-link\"><span class=\"lwrp-list-link-title-text\">Interconnection Technology from Chip Level to Data Center<\/span><\/a><\/li>                <\/ul>\r\n                        <\/div>\r\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Core Port Ratio Definition:&nbsp;Core port ratio refers to the proportional relationship between the ports on network devices (primarily switches) used to connect&nbsp;Computing Nodes (GPU Servers) and the ports used for Internal Network Interconnection when building GPU clusters (e.g., for AI training, HPC). The Core Ratio Problem:&nbsp;The question is: Given a switch&#8217;s (usually a Leaf switch) [&hellip;]<\/p>\n","protected":false},"author":227,"featured_media":18659,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","_wpscppro_dont_share_socialmedia":false,"_wpscppro_custom_social_share_image":0,"_facebook_share_type":"default","_twitter_share_type":"default","_linkedin_share_type":"default","_pinterest_share_type":"default","_linkedin_share_type_page":"default","_instagram_share_type":"default","_medium_share_type":"default","_threads_share_type":"default","_google_business_share_type":"default","_selected_social_profile":[],"_wpsp_enable_custom_social_template":false,"_wpsp_social_scheduling":{"enabled":false,"datetime":null,"platforms":[],"status":"template_only","dateOption":"today","timeOption":"now","customDays":"","customHours":"","customDate":"","customTime":"","schedulingType":"absolute"},"_wpsp_active_default_template":true},"categories":[2,29],"tags":[],"class_list":["post-18654","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-networking"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.13 (Yoast SEO v25.8) - 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